University of Cambridge > Talks.cam > Engineering Safe AI > Decision Theory for AI safety

Decision Theory for AI safety

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Adrià Garriga Alonso.

Decision theory is the study of how rational agents should act in idealised situations. While it has important implications for the design and analysis of AI agents, there are still major unresolved problems. The two leading decision theories – Causal Decision Theory and Evidential Decision Theory – both seem to fail in some cases. Functional Decision Theory, an alternative proposed by researchers at MIRI , has some appealing properties but remains underspecified. In this talk I will explore the importance of decision theory to AI safety, explain the three theories mentioned above, and evaluate various objections which have been raised, and responses to them.

You can read more about the topic here: https://arxiv.org/abs/1710.05060

Talk slides: https://docs.google.com/presentation/d/1hoXUiEbGhzGcC2JkmcMPEpXgXL0Rj19hQdJn46zv1H8/edit#slide=id.p

https://valuealignment.ml/talks/2018-02-07-decision-theory-ai-safety.pdf

This talk is part of the Engineering Safe AI series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.

 

© 2006-2018 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity